Abstract

Meta-analysis is a statistical method of public health relevance that is used to combine the results of individual studies which evaluate the same treatment effect. A test that is commonly used to decide whether the results are homogeneous, and determines model choice for meta-analysis, is called Cochran's Q-test. A major drawback of the Q-test, when the outcomes are normally distributed, is its low power when the number of studies is small, and excessive power when the number of studies is large. In this thesis, we propose a Cochran's Q--test for survival analysis data. Usingsimulations, we examine how the power of Cochran's test changes with different numbers of studies, different weight allocations per study, and the amount of censored observations. We show that the power increases with the increasing number of studies, but lowers with the increasing number of censored observations, and whenever one study comprises a large proportion of the total weight. We conclude that the test of heterogeneity should not be considered as the only determinant of the model choice for meta-analysis. Other methods such as graphical exploration, stratified analysis, or regression modeling should be used in conjunction with the formal statistical test.

Simulation of meta-analysis for assessing the impact of study variability on parameter estimates for survival data

Status:

Unpublished

Abstract:

Meta-analysis is a statistical method of public health relevance that is used to combine the results of individual studies which evaluate the same treatment effect. A test that is commonly used to decide whether the results are homogeneous, and determines model choice for meta-analysis, is called Cochran's Q-test. A major drawback of the Q-test, when the outcomes are normally distributed, is its low power when the number of studies is small, and excessive power when the number of studies is large. In this thesis, we propose a Cochran's Q--test for survival analysis data. Usingsimulations, we examine how the power of Cochran's test changes with different numbers of studies, different weight allocations per study, and the amount of censored observations. We show that the power increases with the increasing number of studies, but lowers with the increasing number of censored observations, and whenever one study comprises a large proportion of the total weight. We conclude that the test of heterogeneity should not be considered as the only determinant of the model choice for meta-analysis. Other methods such as graphical exploration, stratified analysis, or regression modeling should be used in conjunction with the formal statistical test.

Date:

01 June 2006

Date Type:

Completion

Defense Date:

10 April 2006

Approval Date:

01 June 2006

Submission Date:

12 April 2006

Access Restriction:

No restriction; The work is available for access worldwide immediately.